TriQuery: A Query-Based Model for Surgical Triplet Recognition

Artificial intelligence has shown great promise in advancing intelligent surgical systems. Among its applications, surgical video action recognition plays a critical role in enabling accurate intraoperative understanding and decision support. However, the task remains challenging due to the temporal continuity of surgical scenes and the long-tailed, semantically entangled distribution of action triplets composed of instruments, verbs, and targets. To address these issues, we propose TriQuery, a query-based model for surgical triplet recognition and classification. Built on a multi-task Transformer framework, TriQuery decomposes the complex triplet task into three semantically aligned subtasks using task-specific query tokens, which are processed through specialized attention mechanisms. We introduce a Multi-Query Decoding Head (MQ-DH) to jointly model structured subtasks and a Top-K Guided Query Update (TKQ) module to incorporate inter-frame temporal cues. Experiments on the CholecT45 dataset demonstrate that TriQuery achieves improved overall performance over existing baselines across multiple classification tasks. Attention visualizations further show that task queries consistently attend to semantically relevant spatial regions, enhancing model interpretability. These results highlight the effectiveness of TriQuery for advancing surgical video understanding in clinical environments.